Project 2: Multi-Omics of high-risk MM

项目摘要/摘要 虽然我们自己的广泛研究已经证实了被破坏的基因组在多发性硬化中的重要作用 骨髓瘤(MM)，他们也再次强调了认识上的差距，以及免疫的重要性 调控和基因-环境相互作用。确实很可能MM的演变，无论是在之前还是在期间 治疗，是由基因变化和生物扰动驱动的复杂相互作用的结果 环境影响。我们过去的工作也表明，对少数患者进行研究 对于科学理解来说，深度可能与对表面基因组事件的研究一样有益 成千上万的病人。因此，我们将努力产生第一个纵向的转化性临床试验和 多发性骨髓瘤高危人群环境遗传交互作用的综合数据资源。它是 这些继续快速失败的高效治疗的患者的原因仍然是完全的 不透明。需要使用最先进的技术的新的大胆的方法来扭转几十年来的这种状况 缺乏进展。 我们的假设是，对以高分辨率捕捉基因-环境相互作用的数据的分析将揭示 对影响多发性骨髓瘤治疗反应和后续结果的生物途径的洞察。第一, 我们将利用经过仔细研究和同质治疗的高危人群在一年中 具有大型对照临床数据库和生物信息库的第二阶段临床试验，为每个患者派生一个 随着时间的推移，与临床结果相联系的环境基因相互作用的详细地图。第二，我们将执行一项 一系列复杂的分析，以确定MM相关的变化在基因组，转录组， 表观基因组、免疫环境、蛋白质组、脂组和代谢组。第三，我们将在以下位置研究这些样本 当今技术上可行的最高分辨率，并寻求定义基因-环境相互作用的变化 随着时间的推移，这与治疗反应有关。最后，捕捉这些相互作用的高分辨率数据 变化和临床反应数据将联系在一起，以提高我们对潜在机制的理解 MM在患者中的变异性与疾病结局有关。这一全面的资源将使 更加个性化的MM临床监测和治疗方法

PROJECT SUMMARY/ABSTRACT While our own extensive studies have confirmed the significant role of the disrupted genome in Multiple Myeloma (MM), they have also re-emphasized the gaps in understanding, and the importance of immune regulation and gene-environment interaction. It is indeed likely that the evolution of MM, both before or during therapy, is the result of a complex interplay of biological perturbations driven by genetic changes and environmental influences. Our past work has also demonstrated that studying small numbers of patients at great depth can be as rewarding for scientific understanding as studies of superficial genomic events in thousands of patients. Thus we will strive to generate the first, longitudinal, translational clinical trial and comprehensive data resource of environmental genetic interactions for the highest-risk MM population. It is these patients who continue to rapidly fail highly effective therapeutics for reasons which are still completely opaque. New and bold approaches using state-of-the-art technology are required to reverse this decades-old lack of progress. Our hypothesis is that analysis of data capturing gene-environment interactions at high resolution will reveal insights into biological pathways influencing MM responsiveness to therapy and subsequent outcomes. First, we will leverage a carefully studied and homogeneously treated high-risk group of “double hit” patients in a Phase 2 clinical trial with large control clinical databases and bio-repositories to derive, for each patient, a detailed map of environmental gene interactions linked to clinical outcome over time. Second, we will perform a series of complex analyses to identify MM-associated changes in and across the genome, transcriptome, epigenome, immune environment, proteome, lipidome and metabolome. Third, we will study these samples at the highest resolution technically feasible today, and seek to define gene-environment interaction changes over time that associate with response to therapy. Finally, high resolution data capturing these interaction changes and clinical response data will be linked to improve our understanding of the mechanisms underlying MM variability among patients in regards to disease outcomes. This comprehensive resource will enable a more individualized approach to clinical surveillance and therapy for MM.